Use the help menu to explore what the series
gold,woolyrnqandgasrepresent. These are available in theforecastpackage.
- Use
autoplotto plot each of these in separate plots.
- What is the frequency of each commodity series? Hint: apply the
frequency()function.
## [1] 1
## [1] 4
## [1] 12
- Use
which.max()to spot the outlier in thegoldseries. Which observation was it?
## [1] 770
Download the file
tute1.csvfrom the book website, open it in Excel (or some other spreadsheet application), and review its contents. You should find four columns of information. Columns B through D each contain a quarterly series, labelled Sales, AdBudget and GDP. Sales contains the quarterly sales for a small company over the period 1981-2005. AdBudget is the advertising budget and GDP is the gross domestic product. All series have been adjusted for inflation.
- You can read the data into R with the following script:
- Convert the data to time series
(The
[,-1]removes the first column which contains the quarters as we don’t need them now.)
- Construct time series plots of each of the three series
Check what happens when you don’t include
facets=TRUE.
Download some monthly Australian retail data from the book website. These represent retail sales in various categories for different Australian states, and are stored in a MS-Excel file.
- You can read the data into R with the following script:
The second argument (
skip=1) is required because the Excel sheet has two header rows.
- Select one of the time series as follows (but replace the column name with your own chosen column):
- Explore your chosen retail time series using the following functions:
autoplot,ggseasonplot,ggsubseriesplot,gglagplot,ggAcf
Can you spot any seasonality, cyclicity and trend? What do you learn about the series?
In this example (and yours might be different), there is clearly an increasing trend, strong seasonality, and increasing variance as the level increases. There is little evidence of cyclicity.
Create time plots of the following time series:
bicoal,chicken,dole,usdeaths,lynx,goog,writing,fancy,a10,h02.
- Use
help()to find out about the data in each series.- For the
googplot, modify the axis labels and title.
Use the
ggseasonplotandggsubseriesplotfunctions to explore the seasonal patterns in the following time series:writing,fancy,a10,h02.
- What can you say about the seasonal patterns?
- Can you identify any unusual years?
December is much higher than the other months, presumably due to summer holidays in Queensland, or possibly Christmas. The last few years show much higher sales than previous years, but this is probably just due to the exponential trend in the data.
Use the the following graphics functions:
autoplot,ggseasonplot,ggsubseriesplot,gglagplot,ggAcfand explore features from the following time series:hsales,usdeaths,bricksq,sunspotarea,gasoline.
- Can you spot any seasonality, cyclicity and trend?
- What do you learn about the series?
The
arrivalsdata set comprises quarterly international arrivals (in thousands) to Australia from Japan, New Zealand, UK and the US.
- Use
autoplot,ggseasonplotandggsubseriesplotto compare the differences between the arrivals from these four countries.- Can you identify any unusual observations?
The seasonal plots show the difference in seasonal patterns from the four source countries. The peaks for from UK and the US happen in Q1 and Q4 which include the summer period in Australia, Christmas and New Year’s holiday period with Q2 and Q3 being the troughs. For Japan peaks occur mostly in Q1 but also Q3 reflecting both peak arrivals in summer but also winter which possibly correspond to winter skying season or visiting northern Australia in during the dry season. The one source country that is very different is New Zealand. Peak arrivals from New Zealand occur during the Q3 followed by Q2 and Q4. Unlike all other source countries, the trough clearly occurs during Q1 the January (summer) quarter. The seasonal plots are also useful, revealing anomalies or one-off events. For example, in the US plot, the peak arrivals for all July quarters occurred in 2000 during the Sydney Olympic games.
1991:Q3 is unusual for US (Gulf war effect?)
The following time plots and ACF plots correspond to four different time series. Your task is to match each time plot in the first row with one of the ACF plots in the second row.
1-B, 2-A, 3-D, 4-C
The
pigsdata shows the monthly total number of pigs slaughtered in Victoria, Australia, from Jan 1980 to Aug 1995. Usemypigs <- window(pigs, start=1990)to select the data starting from 1990. UseautoplotandggAcfformypigsseries and compare these to white noise plots from Figures 2.17 and 2.18.
mypigs <- window(pigs, start=1990)
autoplot(mypigs) +
xlab("Year") + ylab("") +
ggtitle("Monthly total number of pigs slaughtered in Victoria")
djcontains 292 consecutive trading days of the Dow Jones Index. Useddj <- diff(dj)to compute the daily changes in the index. Plotddjand its ACF. Do the changes in the Dow Jones Index look like white noise?